an empirical analysis of hedge fund performance: the case of australian hedge funds industry

J. of Multi. Fin. Manag. 15 (2005) 377–393

An empirical analysis of hedge fund performance:The case of Australian hedge funds industry

Viet Do, Robert Faff∗, J. WickramanayakeDepartment of Accounting and Finance, P.O. Box 11E, Monash University, Vic. 3800, Australia

Received 15 August 2004; accepted 3 April 2005Available online 7 July 2005

Abstract

This study empirically investigates the performance of Australian hedge funds by extending andmodifying [Capocci, D., Hubner, G., 2004. Analysis of hedge funds performance. Journal of EmpiricalFinance 11, 55–89]. model. This model performs better in explaining Australian hedge fund returnsthan the traditional Fama and French three-factor model. The results show that Australian hedgefund returns have low correlation with market indexes and also outperform standard market indexreturns. We also observe that Australian hedge fund returns are positively related to incentive fees andnegatively related to management fees. Further, managers do not have any significant market timingskill and market conditions do not significantly influence hedge fund performance.© 2005 Elsevier B.V. All rights reserved.

JEL classification:G23; G21; G10

Keywords:Hedge fund performance; Multifactor model; Market timing

1. Introduction

The term “hedge fund” was first introduced in the US in 1949 by Albert Winslow Jones(Fung and Hsieh, 1999). From 1975 onwards, the hedge fund industry experienced a dra-matic increase in terms of number of funds as well as total managed assets. In the late

∗ Corresponding author. Tel.: +61 3 9905 2387; fax: +61 3 9905 2339.E-mail address:[email protected] (R. Faff).

1042-444X/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.mulfin.2005.04.006

378 V. Do et al. / J. of Multi. Fin. Manag. 15 (2005) 377–393

1990s, US researchers started to pay attention to hedge funds and hedge fund performancerelated issues. The study byFung and Hsieh (1997)was one of the earliest to examine theperformance of US hedge funds. Using data for 409 hedge funds and 3327 mutual funds inthe US, they found that mutual fund returns are highly correlated with standard asset returnswhile the opposite holds for hedge fund returns. Hedge fund performance was found to besuperior to that of mutual funds. It was also found that different types of hedge funds producevariations in performance and survivorship bias is a major issue in measuring hedge fundreturns. The study demonstrated that hedge funds are of a totally different nature comparedto mutual funds.

In Australia the hedge fund industry is far less mature than its US counterpart, withthe first local hedge fund starting in the early 1990s. Over the last 3 years, the hedge fundindustry recorded a growth of 20% per annum in terms of total managed funds (Abbott,2003, p. 1). The most recent survey by the Australian Prudential Regulation Authority(APRA) found that there are approximately 200 hedge funds in Australia (APRA, 2003,p. 1). It seems that hedge funds in Australia are becoming undeniably important. Notablyhowever, research on Australian hedge funds remains scarce.

Accordingly, this paper investigates the performance of Australian hedge funds from anumber of angles. First, it explores the correlation of hedge fund returns with other standardmarket returns. Second, we look at the risk-return trade-off of Australian hedge funds incomparison to the market index performance, taking into consideration the fact that hedgefund returns are not normally distributed. Third, we test whether other index returns canexplain Australian hedge fund performance. Fourth, we test whether market conditions haveany influence on hedge fund performance. Fifth, we explain the performance of hedge fundsusing fund characteristics such as incentive fees, management fees and age of the funds.

This paper is organized as follows. Section2 highlights the main difference betweenhedge funds and mutual funds in terms of the regulatory environment in Australia andUS. Section3 briefly reviews the hedge fund literature. Section4 provides a discussionof the data, variables and some limitations related to sample selection. Methodology andprocedure are resolved in Section5. Section6 reports empirical results and followed by asummary of the paper with some suggestions for further research in Section7.

2. Hedge fund legal environment

Hedge funds are viewed as a highly unregulated type of fund that focuses on wealthy(“high net worth”) individuals. The major difference between hedge funds and mutualfunds in the US or managed funds in Australia is the regulatory environment. In the US, theSecurity Act 1993 requires a hedge fund to have no more than 35 “non-accredited” investorsand not to engage in solicitation (Fung and Hsieh, 1999). An “accredited investor” is definedas “an individual who has more than US$ 1 million in financial wealth or earns more thanUS$ 200,000 in the previous 2 years” and non-solicitation is “essentially word of mouthcommunications” (Fung and Hsieh, 1999, p. 135). In the 1990s, a hedge fund in the US wasallowed to have up to 499 investors each of whom was required to have more than US$ 5million in assets; otherwise it would be subject to the Investment Company Act 1940 (Fungand Hsieh, 1999, p. 136).

V. Do et al. / J. of Multi. Fin. Manag. 15 (2005) 377–393 379

In the case of Australia, theCorporation Act (2001)requires all hedge funds to beregistered with the Australian Security and Investment Commission (ASIC) unless thefund has less than 20 investors and will not be professionally promoted or the fund willonly be offered to sophisticated investors. A sophisticated investor is one who has at leastUS$ 2.5 million or earns more than US$ 250,000 in the previous 2 years or the minimumamount payable by an investor is at least US$ 500,000 (Corporation Act, 2001, vol. 3,pp. 234–235). So, if a hedge fund only focuses on sophisticated investors it will not haveto register with ASIC. Such a fund is not under any obligation to comply with ASICregulations and requirements, which seems to be the case for most Australian hedge funds.The regulation-free environment therefore gives hedge fund managers more freedom interms of choosing their strategies. In this regard, Australian hedge funds can be classifiedinto nine categories: absolute return, arbitrage strategy, Australian long/short-long biased,event driven strategies, fund of funds, global macro, long/short strategies, managed futuresand multi-strategies (seeAppendix Afor definitions).

3. Brief review of hedge fund literature

Hedge fund research can be classified into three main categories: hedge fund strategies,returns on hedge funds and modeling hedge fund performance.

Amenc et al. (2003)investigate a tactical asset allocation strategy with a style timingmodel for hedge funds. Using data for 300 US funds from 1995 to 2000, it was found thata perfect timing model with perfect forecasting ability could provide returns three timeshigher than those of average market indexes.

Based on the complex strategies, hedge funds have a very strong performance incentive.Brown et al. (1999, p. 92)reported the average management fees for hedge funds in theUS are 1–2% of managed funds, while incentive fees can range from 5% to 25% over andabove benchmark profits. Unlike mutual funds where the benchmarks are relative bench-marks such as S&P500, hedge funds usually have an absolute benchmark such as LIBORplus premium (Liang, 1999). Empirical studies have found evidence of strong correlationbetween incentive fees and performance (Ackermann et al., 1999; Liang, 1999).

The regulation-free investment environment of hedge funds leads to complex manage-ment strategies and high performance incentives, all affecting hedge fund returns. As aresult, hedge fund returns do not approximate a normal distribution. Previous studies havefound evidence to support this hypothesis: (see for example,Agarwal and Naik, 2002; Fungand Hsieh, 2001; Lo, 2001; Brooks and Kat, 2002; Kouwenberg, 2003). Thus, based onnon-normality characteristics of hedge fund returns the conventional methods of measuringperformance such as the Sharpe ratio or Jensen’s alpha are no longer appropriate (Aminand Kat, 2003). Nonetheless, previous studies have used these techniques and found thathedge funds usually out-perform mutual funds and market indexes (e.g.Ackermann et al.,1999; Liang, 1999).

Gregoriou and Gueyie (2003)propose a modified Sharpe ratio as an alternative measure-ment for hedge fund returns. They enhanced the Sharpe ratio by using a modified value atrisk (MVaR) instead of normal standard deviation as the denominator. The standard VaRonly considers mean and standard deviation while modified VaR takes into consideration


both mean and standard deviation as well as skewness and excess kurtosis. It is claimed tobe a superior measurement tool for hedge fund returns. Testing the modified measurementon 90 live fund of funds in the Zurich capital market from 1997 to 2001,Gregoriou andGueyie (2003)found that large funds are better in controlling risk adjusted performancecompared to small funds. This can be explained by the liquidity of large funds. Comparingthe results between the traditional versus the modified Sharpe ratio,Gregoriou and Gueyie(2003)found the traditional Sharpe ratio to be higher. That means tail risk in the distributionis underestimated by the traditional Sharpe ratio.

Fung et al. (2004)applied the methodology ofRubinstein (1976)andLeland (1999),which measures risk using a higher moment CAPM. They found that incentive fees have asignificant effect on beta and returns that conform to previous findings. However, comparedto the traditional market beta and Jensen’s alpha, the results of the modified measureswere not found to be significantly different. It was concluded that higher moments have nosignificant impact on the performance measurement of hedge funds.

In terms of measuring hedge fund performance, previous studies have shown that dataselection biases can have a considerable affect on the results. The most important bias forhedge funds is survivorship bias. While a bias of around 3% has been documented by someresearchers (Fung and Hsieh, 2000; Brown et al., 1999), Ackermann et al. (1999)suggestthat two biases exist within survivor bias: termination bias and self selection bias, and theyargue that these biases tend to cancel each other out. For example, using data from the HFRdatabase,Liang (2000)found a survivorship bias around 0.6% per year, compared to 0.16%found byAckermann et al. (1999). It is argued that the survivorship bias of 0.16% is evenlower than the range of 0.5–1.4% found in mutual funds, thus contradicting the generalimpression hedge funds are riskier than mutual funds (Liang, 2000).

Another angle to examine for hedge funds is their relationship with other market assets.Previous studies found that portfolio performance improves substantially when hedge fundsare added to a portfolio. For example,Fung and Hsieh (1997)found that a portfolio of 60%US equity and 40% US bonds giving an annual return of 11.55% and a standard deviationof 7.97%, can improve to an average return of 15.92% and standard deviation of 7.10%just by adding hedge funds to a portfolio with equal weights. More recently,Cowell (2003)tested the impact of hedge funds in balancing mutual fund portfolios and found that addinghedge funds to portfolios can improve returns and reduce volatility more effectively thandiversification. This is consistent with the expectation that hedge funds improve portfolioperformance since they have a very low correlation with mutual funds and other assets.

Having understood the major characteristics of hedge funds, a number of studies havedeveloped models to explain their performance. For example, recentlyCapocci and Hubner(2004)examine hedge fund performance by combining several traditional models. Investi-gating the performance of over 6000 hedge funds around the world using traditional CAPM,they found an averageR2

adj of 0.44. Combining the methodology ofCarhart (1997), Famaand French (1998)andAgarwal and Naik (2002), and adding other factors to develop amulti-factor model, they achieved an averageR2

adj of 0.66. Compared to previous models,it is claimed that the new model does a better job in explaining hedge funds performance.

Finally, a number of studies have been conducted to examine hedge fund managers’skills. For example,Brown et al. (1999)and Agarwal and Naik (2000)used US hedgefund data and found no evidence of management skills affecting hedge funds’ performance.


Later,Fung et al. (2002)applied the market timing model to 115 global equity based hedgefunds and also found no evidence of managers’ market timing skills.

4. Data and variables

Data on hedge funds are not as readily available as those on other funds, since mosthedge fund managers who are also investors, typically keep the information to themselves.Moreover, a regulation-free environment implies that hedge fund managers are not obligatedto report on a regular basis. Moreover,Kat (2003)pointed out that most databases reportthe data as supplied by fund managers without any verification. This means that thereexists a possibility of errors and data alteration. Also, as mentioned above, there is theissue of survivorship. Another problem with hedge fund data is the limitation of historicaldata. In Australia, most hedge funds have little or no information prior to 1998. We mustacknowledge this as a limitation of the study.

The data on hedge fund returns in this paper are sampled monthly, as supplied by Investor-info Ltd. Out of the 85 funds in the database, 14 funds have fewer than 12 months of returnsand, thus, are excluded from the dataset. The remaining 71 funds are then categorized intothree common groups of data based on the length of historical data. The first group includesall funds that have more than 24 months of historical returns.Ackermann et al. (1999)suggest that 2 years of monthly data is the minimum sufficient length of data on hedge fundreturns. In our sample there are 50 funds comprising this group. The second common groupincludes funds with more than 31 months of historical data and there are 31 such funds. Thethird common group includes funds with more than 48 months of historical data and thereare 14 of them.1 The data for variables other than hedge fund returns are obtained fromDataStream. We also use Fama and French SMB and HML factors – constructed from ASXindices. Specifically, ASX Small Ordinaries minus ASX100 proxies SMB, while HML iscalculated by subtracting returns of the SSBA (Salomon Smith Barney Australia) growthstock index from returns of the SSBA value stock index.2 Finally, data for hedge fund char-acteristics: management fees, incentive fees, fund age, lock up periods and size are obtainedfrom the Hedge Fund Report 2003 by Investorinfo Ltd.

5. Methodology and model specification

5.1. Time series

We start with the Fama and French three-factor model, which has been well known for itsexplanatory power of mutual fund returns. A multifactor model will then be developed as an

1 There is no special reason for choosing 31 months or 48 months—these are ad hoc choices based on judgment.The main purpose is to have three different groups of data, which are representative of the Australian hedge fundindustry based on the limited data.

2 Faff (2001)constructed proxies for Australian SMB and HML using Russell style indexes. Since the Russellindexes no longer exist, appropriate alternatives were chosen.


extension of the Fama and French three-factor model. FollowingAgarwal and Naik (2002),we use the Lehman BBA corporate bond index as a default factor. There is also a factor fornon-Australian equity investment—the MSCI world index excluding Australia. Consideringthat Australian hedge funds invest in foreign markets, two more factors are included: the JPMorgan global government bond index and the Lehman emerging market index. In addition,following Capocci and Hubner (2004)we use the Goldman Sachs commodity index (ratherthan the gold index) since hedge funds invest in gold as well as other commodities. Finally,the S&P ASX500 index is used as the market index. The 3-month rate on Australian bankbills can be considered as a proxy for a risk free measurement.3 The extended model is asfollows:

XRit = αi + βi1XRmt + βi2SMBt + βi3HMLt + βi4XCBIt + βi5XEMI t

+ βi6XGBIt + βi7XCIt + βi8XWI t + εit (1)

where XRi is the excess return on fund i; XRm the excess return on the market; SMBthe return on the factor-mimicking portfolio for size; HML the return on the factor-mimicking portfolio for book to market equity; XCBI the excess return on Lehmancorporate bond index; XEMI the excess return on Lehman emerging market index; XGBIthe excess return on JP Morgan global government bond index; XCI the excess returnon GSCI Commodity index; XWI is the excess return on MSCI world index excludingAustralia.

We also test if Australian hedge fund managers have significant market timing ability.To this end we use the model applied byFung et al. (2002):

XRit = αi + β1iXRmt + b2i(XRmtDt) + εit (2)

whereD is a dummy variable which takes the value of−1 for a bear month and zerootherwise. A bear (bull) month is deemed to be a month when the market index has a returnlower (higher) than−1% (+1%).

5.2. Cross-sectional

The final part of the study will cross-sectionally test hedge fund risks and hedge fundsreturns against their own characteristics (Kouwenberg, 2003) based on the following generalspecifications:

Hedge fund returns volatility =f(mgment fee, incentive fee, age, size, holding period, float);Hedge fund returns =f(management fee, incentive fee, age, size, holding period, float).

As discussed in the literature, various fund characteristics such as the incentive fee, fundage, fund size and holding period have been used to help explain hedge fund performance.Age is measured in months from the date of inception. Management fee and Incentive feeare measured in %. Fund size is measured as the natural logarithm of $m. Holding periodis the number of days that investors have to give notice to a fund before a redemption can

3 As for the international HML and the momentum factor,Capocci and Hubner (2004)found they do notsignificantly influence hedge fund returns and so they will not be included in our model.


take place. Float is a dummy variable for benchmark factor measurement, which is added inthis study. It takes a value of 1 when a fund has a floating benchmark4 and zero if a floatingbenchmark is not specified or it has no floating benchmark. Funds in the later category tendto be absolute return funds.5 This factor allows us to test if there are any differences in termsof returns and return volatility between funds with floating benchmarks versus those withabsolute return targets.

The funds returns will be measured in two forms: Sharpe ratio and modified Sharperatio. Management fees and incentive fees are expected to have positive relationship withthe funds’ returns. The argument is straightforward—investors who pay more for theirfunds to be managed expect to have higher returns. Hedge fund returns volatility is alsomeasured in two forms: the return standard deviation and CAPM betas. Incentive fee isexpected to be positively related to returns volatility. Previous studies report incentivefees of US hedge funds ranging from 5% to 25% of over and above benchmark profits(Brown et al., 1999, p. 92). With such high incentive fees, it is hypothesized that man-agers take extra risks for extra returns. That is, the higher the incentive fee, the higher therisk.

Table 1, Panel A presents some descriptive statistics for the 50 hedge funds that areavailable from Investorinfo’s Hedge Fund Report 2003. The average age of the funds is 37months, with the longest historical data of 78 months. The average holding period is around1 month and the average incentive fees are around 14% of over benchmark performance.The average fund size is US$ 77 million, with a range of US$ 3 million–1 billion.

6. Empirical results

6.1. Descriptive statistics and correlations

Panel B ofTable 1contains some descriptive statistics of group two of hedge funds inour sample (that is, the group of 31 funds over the period June 2001 to December 2003).The funds are categorized based on funds’ strategy. We see that the average return of theAbsolute return fund is very high compared to other strategies. However, it is noted thatthere is only one fund in this category. Long/short funds produce the next best performancewith an average return of 1.17% per month or around 14% per year. Fund of funds andmulti-strategy are lowest return fund strategies. Fund of funds provide a return of around0.56% per month and multi-strategy funds have average returns of 0.68% per month. TheJarque-Bera statistic for most of the fund strategies indicate that hedge fund returns inAustralia are not normally distributed.

Panel C ofTable 1presents the correlation between regression variables. The highestcorrelation is 0.76 between HML and GBI and the lowest is -0.735 between HML andRmt. Less than 25% of the correlations are in absolute value higher than 0.5. The remain-ing cases have an average correlation of lower than 0.2, too low to raise any question ofmulticollinearity.

4 A floating benchmark is a benchmark based on floating indexes such as MSCI world index.5 Absolute returns funds tend to have a fixed benchmark – for example, a required rate of return of 15%.


Table 1Some descriptive statistics

Age(months)

Holdingperiod(days)

Incentivefees (%)

Managementfees (%)

Modifiedsharpe

Size($m)

Sharpe Standarddeviation

Panel A: hedge fund characteristicsMean 37.12 33.43 13.98 1.31 0.17 77.78 0.32 0.02Median 33.00 30.00 15.00 1.25 0.11 30.20 0.28 0.01Maximum 78.00 90.00 20.50 2.40 2.30 1000.00 1.00 0.06Minimum 14.00 0.00 0.00 0.80 −0.25 3.00 −0.22 0.00Standard

deviation16.89 16.11 6.70 0.37 0.34 157.00 0.28 0.02

Skewness 0.96 1.52 −0.69 0.89 4.96 4.93 0.43 1.22Kurtosis 3.13 8.21 2.38 3.54 31.35 29.13 3.06 3.08

Fundstrategy

No. offunds

Mean Median Standarddeviation

Minimum Maximum Skewness Kurtosis Jarque-Bera

Panel B: Australian hedge fund returns—June 2001 to December 2003Absolute

return1 0.0155 0.0119 0.0289 −0.0239 0.0922 0.680 2.906 2.401

Arbitrage 2 0.0088 0.0073 0.0072 −0.008 0.0246 −0.066 3.023 0.082Event

driven1 0.0088 0.0074 0.0104 −0.013 0.04 0.856 4.553 6.909

Fund offunds

13 0.0056 0.0055 0.0126 −0.0926 0.1197 −0.649 3.974 16.101

Long/short 6 0.0117 0.0132 0.0395 −0.0957 0.2062 0.082 3.322 4.420Managed

futures3 0.0084 0.0043 1.2860 −0.0811 0.1543 0.572 3.417 2.115

Multi-strategy

5 0.0068 0.0073 0.0114 −0.0425 0.0463 −0.700 5.540 70.404

Total funds 31 0.0094 0.0081 0.1994 −0.0957 0.2062 0.110 3.819 14.633

Rm GBI CI SMB HML EMI CBI

Panel C: correlations between variablesRm 1.000GBI −0.584 1.000CI 0.037 −0.154 1.000SMB 0.021 −0.311 −0.289 1.000HML −0.735 0.760 −0.108 −0.244 1.000EMI 0.558 −0.508 0.124 −0.216 −0.667 1.000CBI −0.173 0.380 0.139 −0.715 0.388 −0.035 1.000WI −0.148 0.263 0.208 −0.623 0.229 −0.058 0.612

This table reports a range of descriptive statistics relevant to the Australian hedge sample analyzed in this study.Panel A reports basic hedge fund characteristics and the data represent the 50 funds reported in the InvestorinfoHedge Fund Report 2003. Panel B relates to hedge fund returns for all funds with valid data, sampled monthlyfrom June 2001 to December 2003. Funds are classified into the major strategies described inAppendix A.Panel C reports correlations between the following variables (sampled monthly),Rm: return of the market; GBI:return of JP Morgan global government bond index; CI: return of GSCI Commodity index; SMB: the factor-mimicking portfolio for size; HML: the factor-mimicking portfolio for book to market equity; EMI: returns ofLehman emerging market index; CBI: returns of Lehman corporate bond index; WI: return on MSCI world indexexcluding Australia.


6.2. Sharpe ratio performance

In unreported results, we calculated the Sharpe ratio and modified Sharpe ratio (Gregoriouand Gueyie, 2003) for the sample of 31 funds and for the S&P ASX500 index and MSCIworld index.6 There is quite a clear-cut result–while the two indexes have negative Sharperatios, the vast majority of the hedge funds have a positive Sharpe ratio. This implies adown turn market period and that hedge funds seem to perform well in such circumstances.This is the situation when the non-normality of hedge fund returns ignored. By using themodified Sharpe ratio, hedge funds are still found to outperform the market but now thescales are much smaller. For all hedge funds with a positive conventional Sharpe ratio, themodified Sharpe ratio is considerably lower. On the other hand, the modified Sharpe ratiois higher than the conventional Sharpe ratio for the few negative cases. This result supportsthe view that hedge fund returns are highly not normal and using the conventional Sharperatio can sometimes be very misleading.7

6.3. Hedge fund performance—Fama French and multifactor alphas

Initially, we estimated the traditional Fama and French three-factor model for the group of31 funds. Generally, the adjustedR2 are rather low, with an average value of 0.309.8 Overall,80% of the funds have positive market alpha, with almost 40% of these being significant atthe 5% level. There are 20% of the funds having negative alpha but the estimated values aremostly very close to zero, with only one of them significant. SMB and HML seem to havesignificant influence on the model as well. Almost half of the hedge funds have a significantHML loading, while 30% have a significant SMB loading.

Table 2presents results from the augmented Fama-French, multi-factor model. Theaverage adjustedR-squared improves considerably to 0.43 compared to 0.309 from theprevious model. The highest adjustedR-squared is 0.88. As the number of variables increase,the explanatory power of SMB and HML are decreasing. Only 16% of the funds havesignificant SMB and HML at 5% level. As for other variables, the default factor has a negativeimpact on more than 40% of the cases, and this result is consistent with theCapocci andHubner (2004)findings. The world MSCI excluding Australia, Lehman emerging market,JPM global government bond index, GSCI commodity index and the all ordinary marketindex have a significant contribution in the model for 10–15% of cases. This result indicatesthat there are no dominant factors for Australian hedge fund managers.9

6.4. Market timing model results

Table 3shows the result of estimating the market timing model for Australian hedge fundmanagers. This model requires a longer historical data period to provide reliable results.

6 These results are not reported to conserve space. They are available from the authors upon request.7 Similar results were found for the two other group of hedge funds from 1/2002 to 12/2003 and 1/2000 to

12/2003. Results are available from authors upon request.8 To conserve space, details are suppressed. Results are available from the authors upon request.9 Similar results were found for the two other groups of hedge funds from 1/2002 to 12/2003 and 1/2000 to

12/2003. Results are available from authors upon request.

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Table 2Hedge fund performance in an augmented Fama and French three factor model—June 2001 to December 2003Fund no. Alpha Market HML SMB CI GBI CBI EMI WI AdjR2

Panel A: fund-of-funds1 0.0029 (1.94) −0.0213 (−0.25) −0.0611 (−1.03) 0.1449 (0.79) 0.0222 (0.78) 0.019 (0.25) 0.2452 (1.89) −0.051 (−1.20) −0.0108 (−0.16) 0.26232 −0.0001 (−0.05) −0.0086 (−0.12) −0.1887 (−2.12) 0.0059 (0.03) −0.0491 (−1.33) 0.0479 (0.51) 0.0801 (0.51) 0.0093 (0.19) 0.2455 (1.74) 0.61773 −0.0003 −0.0255 (−0.54) −0.0123 (−0.47) −0.0933 (−0.79) −0.0011 (−0.07) −0.0319 (−0.78) 0.0182 (0.24) 0.0386 (1.78) 0.0775 (1.32) 0.38554 0.0014 (0.92) 0.0129 (0.26) 0.0228 (0.77) −0.0792 (−0.95) 0.0099 (0.50) −0.0327 (−0.55) 0.0256 (0.28) 0.0705 (2.61) 0.0766 (1.51) 0.40255 −0.0014 (−1.73) 0.1203 (2.43) −0.0041 (−0.16) 0.2469 (2.28) 0.0288 (1.61) 0.026 (0.57) 0.0509 (0.54) 0.0324 (1.12) −0.0383 (−1.10) 0.68256 −0.0041 (−1.64) 0.0009 (0.01) 0.0133 (0.16) 0.9679 (4.14) 0.0427 (1.20) −0.0408 (−0.42) 0.1609 (0.79) 0.2704 (3.71) 0.127 (1.21) 0.67387 0.0021 (0.98) 0.0852 (1.05) −0.0163 (−0.26) 0.1002 (0.60) 0.0028 (0.10) −0.0299 (−0.34) 0.2568 (1.96) 0.0772 (1.38) 0.1118 (1.36) 0.4158 0.0133 (1.42) 0.4861 (1.07) 0.3691 (1.18) −0.7907 (−0.62) 0.4063 (3.12) 0.2858 (0.61) −0.1001 (−0.20) −0.2856 (−1.14) 0.5792 (1.44) 0.64799 0.0037 (2.05) 0.037 (0.62) 0.0064 (0.14) −0.0533 (−0.41) 0.0082 (0.42) −0.0804 (−1.46) −0.061 (−0.55) 0.0291 (0.77) 0.0893 (1.13) 0.2514

10 0.0015 (1.10) 0.0205 (0.33) −0.0082 (−0.31) 0.004 (0.04) −0.0084 (−0.40) −0.0682 (−1.19) 0.0877 (0.83) 0.0798 (2.06) 0.0635 (0.90) 0.470111 −0.0002 (−0.17) −0.0032 (−0.04) −0.0205 (−0.59) −0.0249 (−0.21) 0.0028 (0.15) 0.0084 (0.18) 0.0171 (0.28) 0.0183 (0.53) 0.0037 (0.11) 0.108512 −0.0002 (−0.17) −0.1158 (−1.66) −0.0739 (−2.22) −0.2194 (−2.81) −0.0002 (−0.01) 0.0017 (0.04) −0.0371 (−0.34) 0.0296 (0.96) 0.0581 (1.28) 0.477113 −0.0046 (−1.29) −0.1066 (−0.64) −0.1661 (−1.93) 0.0426 (0.11) −0.0532 (−0.89) −0.0923 (−0.64) 0.0603 (0.23) −0.0517 (−0.48) 0.1471 (0.85) 0.3173

Panel B: long-short hedge funds14 0.0092 (0.80) 0.522 (0.82) 0.1583 (0.57) 1.6538 (1.11) −0.008 (−0.07) −0.3427 (−0.96) −0.237 (−0.34) −0.1527 (−0.49) 1.5539 (1.62) 0.183215 0.0043 (0.77) −0.1191 (−0.40) −0.5042 (−2.86) 0.2572 (0.29) 0.0108 (0.08) −0.0057 (−0.02) 0.2292 (0.33) −0.0332 (−0.14) −0.0936 (−0.22) 0.558316 0.0109 (2.04) 0.6801 (1.85) −0.0979 (−0.37) −0.0128 (−0.02) 0.0917 (0.55) −0.0798 (−0.25) 0.364 (0.62) 0.0212 (0.08) −0.1627 (−0.59) 0.545617 0.0021 (0.32) 0.8732 (4.30) 0.4748 (3.03) 0.2102 (0.40) 0.1806 (2.38) −0.8207 (−2.92) 0.0318 (0.06) −0.2154 (−1.19) 0.4333 (1.58) 0.494318 0.0116 (2.55) 1.1507 (4.12) −0.0463 (−0.26) 0.803 (1.83) −0.0265 (−0.35) 0.3397 (1.46) −0.3679 (−0.98) −0.0027 (−0.02) 0.0183 (0.09) 0.882819 0.007 (1.44) −0.1557 (−0.73) −0.1442 (−1.23) 0.0602 (0.09) 0.0021 (0.03) 0.0527 (0.29) 0.667 (1.93) −0.0781 (−0.89) −0.0015 (−0.01) 0.1838

V.Doetal./J.o

fMulti.F

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anag.1

5(2005)377–393

387Panel C: managed futures hedge funds

20 0.0135 (1.51) 0.606 (0.90) −0.1162 (−0.33) −1.053 (−1.04) 0.3397 (1.33) 1.0214 (2.31) −0.1493 (−0.14) −0.0591 (−0.29) 0.4529 (0.78) 0.46921 0.001 (0.38) −0.012 (−0.10) 0.0381 (0.54) −0.857 (−2.38) −0.0364 (−0.91) 0.0556 (0.44) −0.0109 (−0.04) 0.0442 (0.39) −0.3217 (−2.75) 0.338122 0.0008 (0.19) 0.1127 (0.43) 0.1138 (0.67) −0.6025 (−0.84) −0.0235 (−0.41) −0.0826 (−0.38) −0.4403 (−0.80) −0.0263 (−0.17) 0.1003 (0.43) 0.1346

Panel D: multi-strategy hedge funds23 0.0008 (1.28) 0.0247 (0.64) −0.0251 (−1.01) −0.0906 (−0.93) 0.0284 (1.96) −0.0229 (−0.56) 0.014 (0.20) 0.0351 (1.69) 0.0707 (1.05) 0.614224 −0.0017 (−0.59) −0.0025 (−0.04) −0.0732 (−0.91) 0.2274 (1.03) −0.0145 (−0.45) 0.0113 (0.13) −0.0485 (−0.55) 0.0396 (1.23) 0.2323 (1.61) 0.420825 0.0095 (2.77) 0.2362 (1.36) −0.0655 (−0.56) −0.4005 (−1.06) 0.034 (0.49) 0.015 (0.16) −0.0428 (−0.15) −0.1455 (−1.46) −0.0496 (−0.29) 0.344926 0.0044 (3.00) 0.1017 (1.48) −0.0583 (−1.01) 0.1127 (0.65) 0.0143 (0.66) −0.0212 (−0.40) 0.1228 (1.03) 0.008 (0.29) 0.0584 (0.53) 0.676127 0.0014 (0.35) 0.5005 (2.20) 0.1199 (0.90) 0.3443 (0.74) 0.0197 (0.34) −0.0832 (−0.44) −0.0352 (−0.11) −0.0838 (−0.79) 0.2388 (1.68) 0.3624

Panel E: ‘other’ hedge funds28 0.0028 (0.67) −0.242 (−1.25) −0.1826 (−1.76) −0.7306 (−0.94) −0.165 (−1.81) −0.1726 (−0.59) −0.5068 (−1.02) 0.243 (1.01) 0.5467 (1.37) 0.36729 0.0046 (2.26) −0.109 (−1.15) −0.068 (−0.98) −0.1903 (−1.12) −0.0181 (−0.76) −0.0823 (−1.20) 0.0897 (0.67) 0.042 (0.87) −0.0488 (−0.55) 0.341630 0.0036 (1.55) −0.0935 (−1.15) −0.0477 (−0.82) −0.2503 (−1.43) −0.018 (−0.88) −0.0925 (−1.40) 0.043 (0.30) 0.0611 (1.22) −0.0369 (−0.36) 0.342431 0.006 (2.63) 0.2779 (2.70) 0.0986 (1.25) −0.0572 (−0.24) 0.0049 (0.17) −0.1898 (−2.01) −0.0074 (−0.03) −0.0306 (−0.69) 0.0261 (0.47) 0.4842

This table reports the outcome for estimating an augmented Fama-French factor model for our sample of hedge funds: see Eq.(1), where: XRi is the excess return onfund i; XRm the excess return on the market; SMB the return on the factor-mimicking portfolio for size; HML the return on the factor-mimicking portfolio for book tomarket equity; XCBI the excess return on Lehman corporate bond index; XEMI the excess return on Lehman emerging market index; XGBI the excess return onJPMorgan global government bond index; XCI the excess return on GSCI Commodity index; XWI the excess return on MSCI world index excluding Australia.


Table 3Market timing performance of hedge fund sample—January 2000 to December 2003

Fund no. Alpha Beta1 Beta2 AdjR2

A 0.004 (1.31) 0.1497 (1.44) −0.0805 (−0.42) 0.1659B 0.0046 (1.46) −0.0103 (−0.11) −0.2411 (−1.23) 0.1373C 0.0039 (2.86) −0.0667 (−1.51) −0.1007 (−1.50) 0.0391D 0.0071 (5.26) −0.1593 (−4.09) −0.2314 (−3.11) 0.1406E 0.0072 (1.50) −0.0728 (−0.77) −0.4428 (−2.24) 0.1784F 0.0058 (1.10) −0.0266 (−0.09) −0.4835 (−1.28) 0.125G 0.009 (4.76) −0.1113 (−1.57) −0.3428 (−3.03) 0.1764H −0.0017 (−0.48) −0.0185 (−0.13) 0.2384 (1.00) 0.0898I 0.0285 (2.20) 0.0944 (0.18) −1.4835 (−1.99) 0.2799J 0.0183 (2.39) 0.6942 (1.81) −0.4953 (−1.02) 0.3988K 0.0034 (0.43) −0.187 (−0.73) −0.0061 (−0.01) 0.0315L −0.0054 (−0.45) −0.2324 (−0.56) 1.2508 (2.18) 0.2952M 0.0075 (0.57) 0.4743 (0.93) 0.046 (0.06) 0.1163N 0.0148 (2.58) 1.0857 (5.74) −0.1425 (−0.36) 0.7759

This table reports the outcome for estimating a timing model for our sample of hedge funds: see Eq.(2), whereXRi is the excess return on fund i; XRm the excess return on the market;D is a dummy variable which takes thevalue of−1 for a bear month and zero otherwise. A bear (bull) month is deemed to be a month when the marketindex has a return lower (higher) than−1% (+1%).

Therefore, we only run on the longest group of (14) funds over the period January 2000 toDecember 2003.

The alpha in this model represents the hedge fund managers’ ability to select outper-forming stocks. Twelve out of 14 funds having positive market alpha and 50% of thesecases are significant at the 5% level. It provides some evidence that this sample of hedgefund managers do have superior relative performance by means of security selection. Inrelation to market timing ability, 12 out of the 14 funds have negative beta2, 5 of which arestatistically significant at the 5% level. The two positive ‘timing’ betas are not significant.So, based on this evidence, it can be concluded that Australian hedge fund managers tendto have superior relative performance by means of security selection but not from markettiming. Indeed, similar to prior mutual fund findings, if anything the positive selectivityskills of fund managers are countervailed by poor market timing.10

6.5. Performance in relation to fund characteristics

Table 4presents the results from the cross-sectional regression of the Sharpe ratio andmodified Sharpe ratio against other fund variables. For both conventional Sharpe and mod-ified Sharpe ratio regressions, all variables except holding period and age have a significantimpact on fund performance. Incentive fee is significant at the 5% level for Sharpe ratiomeasurement and at the 1% level for the modified Sharpe ratio analysis. The positive signimplies that funds with higher incentive fees tend to perform better than funds with lower

10 It should be noted that these results may reflect that some of these hedge funds have been subjected to thewell-known “tricks” to increase the Sharpe ratio–such as, by creating short synthetic call options to lower “risk”and increase “return”. We thank an anonymous referee for this helpful suggestion.


Table 4Regression of hedge fund returns on hedge fund characteristics

Variable Dep Var: Conventional Sharpe ratio Dep Var: Modified Sharpe ratio

Coefficient t-Stat Prob. Coefficient t-Stat Prob.

Constant 0.4249* 1.7062 0.0977 0.1312* 1.7113 0.0967AGE −0.0043 −1.5474 0.1316 −0.0012 −1.3567 0.1844Holding period −0.0002 −0.0678 0.9463 −0.0003 −0.3200 0.7510Incentive fee 0.0162** 2.2137 0.0341 0.0063*** 2.8130 0.0083MGT fee −0.2309* −1.8460 0.0742 −0.0780* −2.0249 0.0513LG size 0.0666* 1.7449 0.0906 0.0188 1.6029 0.1188Float −0.2174** −2.4004 0.0224 −0.0580** −2.0797 0.0456

Rsquared 0.3147 0.3063Adj Rsquared 0.1862 0.1762F-statistic 2.4487 2.3545Prob (F-statistic) 0.0462 0.0537

This table reports the outcome for estimating a cross-sectional regression model for a sub-sample of 39 hedgefunds, where the dependent variable is hedge fund returns proxied in two alternate ways: conventional Sharpe ratioand modified Sharpe ratio. Independent variables in the model are: age (months); holding period (the number ofdays that investors have to give notice to a fund before a redemption can take place); incentive fee (%); managementfee (%); size (natural log of $m); and Float (a dummy variable for benchmark factor measurement, that takes avalue of 1 when a fund has a floating benchmark and zero if a floating benchmark is not specified or it has nofloating benchmark).

* Significant at 10% level.** Significant at 5% level.

*** Significant at 1% level.

incentive fees. A 1% point increase in incentive fees lead to an increase of 0.0162 (0.0063)in Sharpe (modified Sharpe) ratio. Management fees also have a significant impact for bothmeasures. Specifically, the negative coefficients mean that a fund with higher managementfees tends to provide lower returns. More specifically, a fund with 1% point higher man-agement fees has 0.2309 (0.078) lower average Sharpe (modified Sharpe) ratio. This isanother indication of how the conventional Sharpe ratio may not be a good indicator whenmeasuring hedge fund performance. These two findings are consistent withAckermannet al. (1999).

Notably, a number of other interesting results are found on this sample. First, the coeffi-cient on the Float dummy variable is negative and significant at 5% level for both cases. Theimplication is that absolute return funds out-perform floating benchmark funds. Fund size isalso found to be significant at the 10% level for the conventional Sharpe but not for the mod-ified Sharpe ratio. Basically, the larger the fund, the better the performance but the findingis weak. Generally, these findings are similar to those reported byAckermann et al. (1999).

Table 5shows the results for similar cross-sectional regressions with return volatil-ity (standard deviation) and fund risk (CAPM beta) as the dependent variables. It is notsurprising that incentive fee significantly influences return volatility. Again, this result isconsistent withAckermann et al. (1999). Fund age and size are also statistically significant.The results suggest that older funds tend to take more risk, while larger funds have lowerrisk levels on an average.


Table 5Regression of hedge fund risk on hedge fund characteristics

Variable Dep Var: Standard deviation Dep Var: CAPM beta

Coefficient t-Stat Prob. Coefficient t-Stat Prob.

Constant 0.0015 0.1118 0.9117 −0.2407 −0.8366 0.4090Age 0.0003** 2.1419 0.0399 0.0078** 2.4270 0.0210HOLDING PERIOD −0.0001 −0.6395 0.5271 −0.0655 −1.4842 0.1475INCENTIVE FEE 0.0009** 2.2157 0.0339 0.0065 0.7759 0.4435MGT FEE 0.0060 0.9081 0.3706 −0.0326 −0.2258 0.8228LGSIZE −0.0040* −1.9962 0.0545 0.0081** 2.2005 0.0351FLOAT 0.0112 2.3334 0.0261 0.0373 0.3562 0.7241Rsquared 0.5155 0.3608Adj Rsquared 0.4247 0.2410F-statistic 5.6754 3.0107Prob. (F-statistic) 0.0004 0.0190

This table reports the outcome for estimating a cross-sectional regression model for a sub-sample of 39 hedgefunds, where the dependent variable is hedge fund risk proxied in two alternate ways: standard deviation of returnsand CAPM beta. Independent variables in the model are: age (months); holding period (the number of days thatinvestors have to give notice to a fund before a redemption can take place); incentive fee (%); management fee(%); size (natural log of $m); Float (a dummy variable for benchmark factor measurement, that takes a value of1 when a fund has a floating benchmark and zero if a floating benchmark is not specified or it has no floatingbenchmark).

* Significant at 10% level.** Significant at 5% level.

Turning to the CAPM beta analysis, the results show that incentive fee is no longersignificant. However, age and size are found to be (positively) statistically significant inexplaining systematic risks. Specifically, it appears that older and larger funds tend to takeon higher systematic risks.

7. Summary and conclusion

With high average performance, hedge funds are becoming more and more attractive toindividual investors as well as Australian fund managers. With an average growth of 20%per annum in total managed funds over the last 3 years, hedge funds are becoming one of thefastest growth alternative investments in Australia. Using a sample of 85 funds with totalassets under management of over US$ 6.8 billion (Hedge Fund Report 2003 by InvestorinfoLtd.), we investigate Australian hedge fund performance from a few different perspectives.

First, we test the nature of hedge fund performance. The literature has shown that hedgefunds are very different compared to mutual funds in terms of returns, as well as fundstrategies. As expected, Australian hedge funds also have non-normal returns, which comefrom the complex trading strategies employed. The paper also investigates the correlationsbetween hedge fund returns and those of other standard assets. Our results show that hedgefunds invest in a wide range of sectors/areas and, as such, there is no favorite investment area.

Second, we investigate hedge fund returns using different tools of measurement. It wassuggested in the literature that the conventional Sharpe ratio is not a good performance


measure for hedge funds due to non-normal returns. The current study applied a newmeasure introduced byGregoriou and Gueyie (2003)– the modified Sharpe ratio. Themodified Sharpe ratio takes into account the skewness and kurtosis of returns. It is foundthat the conventional Sharpe ratio reports higher performance compared to the modifiedSharpe ratio. In general, even after taking into account non-normality, our sample of hedgefunds still outperforms the market.

Third, we examined Australian hedge fund performance using the Fama and Frenchthree-factor model and the extended model modified fromCapocci and Hubner (2004)toput it in an Australian perspective. Compared to the Fama and French three-factor model,the modified model is better at explaining Australian hedge fund performance.

Fourth, we test the market timing skills of funds managers and hedge fund performancebased on funds’ characteristics. The results support the conclusion that Australian hedgefund managers do have some degree of superior security selection ability but do not appearto be good at market timing.

Finally, a cross-sectional analysis is run to explain fund performance and risk level byseveral fund characteristics. A number of interesting results are found. Perhaps of greatestnote is that fund performance measured by the Sharpe ratio and the modified Sharpe ratiocan be explained by management fee and incentive fee, which is consistent withAckermannet al. (1999). Regarding the fund risk level, fund age is found to have a positive relationshipwith the level of risk for both risk measurements: standard deviation and CAPM beta.Incentive fee is also found to have a positive relationship with the level of risk but only forstandard deviation analysis.

There are several limitations of the current study regarding data collection and method-ology. As discussed in Section4, the data for hedge funds are still far from perfect.Furthermore, the timeframe of the dataset used in this paper is fairly short compared toother studies that use US hedge funds data. This is due to the limitation of data availabil-ity as well as the nature of Australian hedge funds. The results found in this paper arealso subject to data biases, survivorship bias being the most notable. The Australian hedgefund industry, however, has not matured enough for survivorship bias to be accuratelyassessed.

Acknowledgements

The authors are from Monash University, Victoria, Australia. We would like to thankDavid Sokulsky and Investorinfo Ltd. for providing the hedge fund data. The viewsexpressed in the paper are those of authors who take sole responsibility for any errors oromissions.

Appendix A

Definitions of hedge fund classes based on strategyDefinitions are expressed using the information provided in Hedge Fund Report 2003

by Investorinfo Ltd.


Absolute return: Aim to produce superior risk adjusted return by trading a diversifiedportfolio of developed market currencies using a systematic approach.Arbitrage strategies: Seek arbitrage opportunities in domestic and international marketsas well as in mergers and acquisitions of Australian listed companies.Australian long/short-long biased: Apply a securities selection strategy to pick out secu-rities whose prices do not reflect potential growth. They can short sell 25% of the fund’snet value and invest in futures and option markets.Event driven strategies: Focus on merger and capital growth arbitrage opportunities in theAustralian equity market.Fund of funds: Allocate capital among investment funds with a diversified mix of invest-ment vehicles. This type of fund ensures a greater diversification for investors and allowsthem to get access to other products offered by other hedge funds.Global macro: Attempt to identify mispricing in asset markets by using an integratedglobal modeling system that captures the independencies between asset markets and othermarkets within and between the economies.Long/short strategy: Select short and long positions to maximize risk adjusted returns. Thefunds use their own technique to select short and long positions and the general target is40–65% net long.Managed futures: Apply statistical techniques to design a trading system that suits fundmanagers’ risk and return profiles. This strategy normally concentrates on the establishedfuture markets.Multi-strategies: Apply more than one trading strategy above based on managers’ objec-tives and market conditions.

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